Evaluation-driven development: How to move AI agents from pilot to production

CVS Health leaders Matt Turner and Lagan Khare argue that moving AI agents from promising pilots to reliable production systems requires an AI-native development lifecycle centered on clear specifications, shared context, evaluation harnesses, observability, governance, and business-focused cost measurement rather than model capability alone. Their approach uses structured requirements and dependency maps to guide agent-generated work, golden datasets and regression suites to define acceptable behavior, continuous production monitoring to detect drift, and traceable guardrails, scoped permissions, rollback paths, and purposeful human review to manage risk, particularly in regulated settings. They recommend measuring economics through cost per successful outcome linked to operational KPIs, mapping complete workflows to prevent automation from shifting bottlenecks downstream, and prioritizing durable assets such as high-quality data, workflow design, evaluations, and trust controls over infrastructure that stronger models may eventually replace. Production readiness ultimately depends on having measurable business outcomes, evidence-based release thresholds, observable actions and costs, and explicit conditions for reducing or stopping agent autonomy.

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